The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for providing automatic contour annotation of medical images based on correlations with medical reports.
Medical images, especially labeled (or annotated) images, are difficult and expensive to acquire. Often such labeled images require large expenditures of human effort and resources where a human subject matter expert (SME) must manually identify anatomical structures and characteristics within the medical images and annotate the medical images with identifiers of such anatomical structures and characteristics.
Machine learning algorithms may be trained to classify different medical conditions in medical imaging, such as identifying medical images with anomalies or diseases present in the medical images, and differentiating such medical images showing anomalies and diseases from normal medical images in which such anomalies are present. Such training often requires large sets of annotated or labeled medical images in order for the machine learning algorithm to reach convergence. Unfortunately, however, most sources of medical images provide the medical images as unlabeled or non-annotated medical images.